skills.rest
mims-harvard
mims-harvardOFFICIAL
Explore all skills by this author →
Website𝕏@marinkazitnikGitHub

tooluniverse-network-pharmacology

Constructs compound-target-disease networks for drug repurposing and polypharmacology analysis.

1.7k|254|Updated Mar 3, 2025
Search Tags:#OpenTargets#drug repurposing#protein interactions#STRING#polypharmacology#network pharmacology#network proximity
One-click install
npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-network-pharmacology
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-network-pharmacology
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/plugins/tooluniverse/skills/tooluniverse-network-pharmacology
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-network-pharmacology

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires networkx, and includes scripts (resource) components.

What problem does it solve?

Determining whether a drug can be repurposed for a new disease requires assembling evidence across many disconnected databases (targets, PPI networks, clinical trials, adverse events) and computing network proximity statistics that no single API provides. This Skill orchestrates that entire workflow and produces a scored, evidence-graded report.

Core Features & Use Cases

  • Compound-Target-Disease Network Construction: Resolves entities to ChEMBL, DrugBank, PubChem, Ensembl, and MONDO/EFO identifiers, then builds C-T, T-D, C-D, and T-T edges using 60+ ToolUniverse tools across STRING, OpenTargets, DGIdb, CTD, and ChEMBL.
  • Network Proximity Scoring: Computes the Guney/Barabasi closest-distance Z-score with a degree-matched random null via a bundled script against the full STRING v12 human interactome.
  • Repurposing Candidate Ranking: Ranks candidates with a 0-100 Network Pharmacology Score (proximity, clinical evidence, target-disease association, safety, mechanism) and T1-T4 evidence grading.
  • Use Case: Ask whether metformin can be repurposed for Alzheimer's disease and receive a report with network topology, proximity Z-score, ranked candidates, safety profile from FAERS/FDA data, and clinical trial precedent.

Quick Start

Ask the agent to run a network pharmacology analysis of metformin for Alzheimer's disease and generate the scored repurposing report.

Frequently Asked Questions about tooluniverse-network-pharmacology

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze drug repurposing with network pharmacology?▼

Resolve the drug and disease to database identifiers, retrieve drug targets and disease genes from OpenTargets, DrugBank, and DGIdb, build a PPI network from STRING, then compute the network proximity Z-score. Candidates are ranked by a 0-100 composite score covering proximity, clinical evidence, association, safety, and mechanism.

How is the network proximity Z-score calculated?▼

The bundled network_proximity.py script downloads the STRING v12 high-confidence human interactome, computes the mean closest distance between drug targets and disease genes, and compares it against degree-matched random gene sets. A Z-score below -2 indicates significant proximity.

What databases does network pharmacology analysis use?▼

It queries over 15 sources through ToolUniverse: OpenTargets, STRING, DrugBank, DGIdb, CTD, ChEMBL, PubChem, Ensembl, GWAS, FAERS, FDA, ClinicalTrials.gov, PubMed, EuropePMC, Reactome, and Enrichr, covering targets, interactions, pathways, safety, and literature.

When should I not use network pharmacology analysis?▼

Skip it for simple repurposing lookups without network analysis, single-target validation, or adverse-event-only questions; dedicated skills cover those. It also has limited value for orphan diseases or disconnected networks where proximity cannot be computed meaningfully.

Why does network proximity return a non-significant Z-score?▼

A Z near zero usually means the null model used a small query-centered subnetwork instead of the full interactome, making random sets nearly identical to real ones. Use the full STRING network or a high-limit edge set so the degree-matched null is informative.

Related Skills

Explore More Education & Research Skills→
LeonChaoXLeonChaoX
783

opentargets-database

Query Open Targets Platform for disease–target associations and therapeutic candidates.

Community
Advanced
DrugClawDrugClaw
126

clinical-research-tools

Guide clinical research protocol design, endpoint selection, and reporting guideline adherence.

Official
Intermediate
SologaSologa
1

opentargets-database

Query the Open Targets Platform GraphQL API for target-disease associations and drug data.

Community
Intermediate
OpenSourcePharmaFoundationOpenSourcePharmaFoundation
4

drug-repurposing-strategist

Match OM-associated genes to approved drug targets and rank repurposing candidates.

Community
Advanced